Secure Enterprise AI Architecture
Defense-in-depth security framework protecting production AI systems from indirect prompt injection, data exfiltration, jailbreaks, and adversarial poisoning.
The Core Problem Solved
LLMs inherently blur the line between control logic (system instructions) and untrusted user input, opening severe vulnerabilities to indirect injection via external data.
When To Deploy This Architecture
Customer-facing agents, systems reading public emails/documents, and any AI application with tool-execution privileges.
Architectural Components
- Input Sanitizer & Classifier (NeMo Guardrails / Llama Guard)
- Dual-LLM Architecture (Isolated untrusted data processor vs privileged executive LLM)
- Tool Permission Boundary & Output Whitelisting
- Egress Firewall (Deny-by-default external network access)
- Canary Token Trap System
Data Flow Narrative
Security & Perimeter Control
Zero trust architecture; assumption that all third-party fetched content is malicious; canary tokens embedded in system prompts to alert on extraction attempts.
Governance & Telemetry
Alignment with OWASP Top 10 for LLM Applications and NIST AI Risk Management Framework (RMF).
Identified Failure Modes & Mitigations
High false-positive rate blocking benign user queries; latency degradation through multi-stage guardrail evaluation.